Trackers receive sensor data and determine state vectors associated with detected objects in a search volume. Trackers are used in the real-time tracking of objects including air, surface and space targets. Some tracking scenarios include determining tracks for individual ballistic objects in a dense target environment. Situations where detection density is high, include solid fuel debris cloud environment observed with high-resolution X-band radars, and space debris, for example, associated with a debris cloud from a destroyed satellite. Other situations include, size of object larger than radar resolution (e.g., large objects observed with high-resolution X-band radars). Some situations include combination of above two situations.
One class of real-time trackers use an approach referred to as multiple hypothesis tracking (MHT). Multiple hypothesis trackers recursively process detection data, but associate detections for a relatively short period of time. For example, a multiple hypothesis tracker may rely on detections from two or perhaps three subsequent sensor sweep or sensor update cycles. Such approaches for tracking objects can increase precision through increased detection information in the batch interval. Unfortunately, such approaches are subject to a dramatic (e.g., exponential) increase in computational requirements when a relatively long batch of detections is used to formulate association decisions. Consequently, practical systems rely on relatively short batch periods to make association decisions. Such limitations results in poor performance when, for example, detection density is high, or object detection probability is small. Even greater computational requirements are necessary when objects have multiple scatterers.